Computer network controlled data orchestration system and method for data aggregation, normalization, for presentation, analysis and action/decision making

ABSTRACT

Embodiments disclosed include computer automated systems and methods for aggregating data from a plurality of data sources, such as proxy devices, legacy protocols, devices, applications, machines, sensors, things across locations and user types, or device clouds among devices and applications. The aggregated data is then normalized and the normalized data is analyzed. The analyzing is based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of data sources. And based on the analyzed data, relevant aggregated and normalized data is combined and displayed in a display compatible format. Additionally, user needs are determined based on the analyzed aggregated, normalized data. The user need comprises a need for an item or items comprising at least one of a service, a product, and an upgrade of hardware or software components. Further a provider from a plurality of providers is determined based on the determined user need, and finally a need fulfillment transaction between the user and the provider is initiated.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 14/797,091 filed on 11 Jul. 2015 entitled “COMPUTER NETWORKCONTROLLED DATA ORCHESTRATION SYSTEM AND METHOD FOR DATA AGGREGATION,NORMALIZATION, FOR PRESENTATION, ANALYSIS AND ACTION/DECISION MAKING”the contents of which are incorporated by reference in their entirety.This application bears reference to U.S. application Ser. No. 14/801,326filed 16 Jul. 2015 entitled “SYSTEM AND METHOD FOR CONTEXTUAL SERVICEDELIVERY VIA MOBILE COMMUNICATION DEVICES”, to U.S. application Ser. No.14/801,385 filed on 16 Jul. 2015, entitled “HYBRID SYSTEM AND METHOD FORDATA AND FILE CONVERSION ACROSS COMPUTING DEVICES AND PLATFORMS” and toU.S. application Ser. No. 18/801,446 entitled “UNIVERSAL SECURE IMAGINGWORKFLOW” the contents of which are incorporated by reference in theirentirety.

BACKGROUND

Field

This application relates to platforms for collecting, normalizing,aggregating, and presenting/processing data from and to a wide range ofdevices, machines and applications in a wired or wireless networkedframework.

Related Art

As computerization and networked devices capable of communicating witheach other have become all pervasive, this has resulted in a vast amountand number of disparate data sources. Data sources include sensors,devices, mobile devices, machines, applications, etc. Additionally, datasources comprise multiple vendors, varied models of the sensors,devices, mobile devices, machines and applications. Yet additionally,the devices are run by varied/different Operating Systems (OS),protocols etc. Further compounding the problem is a lack ofstandardization, wherein multiple vendors, models, operating systems,protocols, etc. make it impossible for seamless communication acrossdevice models and brands. Current solutions can and have addressed this.Addressing this challenge requires three key steps—1. Data collection,aggregation and normalization, 2. Analysis based on events, conditions,and trends across disparate data sources, and 3. Decision making/Actionsto be taken, often as a return path or closed loop back to themachines/sensors and or other devices/applications.

However, today's systems offer sequential steps that are time consuming,wherein by the time a decision is taken, the data/conditions may havechanged leading to wrong decisions. Further, solutions available areisolated solutions and tend to be silos of a single context, vendor,device type, machine type, application, etc.

There remains a need for automated, real-time, decision making, based onan analysis of conditions across multiple sources. There remains afurther need in such an analysis for a correlation capability across themultiple sources. There also remains an additional need to perform suchanalysis, correlation, and decision making, in real-time, in anautomated fashion. Embodiments disclosed address the above challenges.

SUMMARY

Embodiments disclosed include a platform for collecting, normalizing,aggregating, and presenting/processing data over a wide range ofdevices, machines and applications in real-time, in a wired or wirelessnetworked framework.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes the system to perform the actions. One or more computerprograms can be configured to perform particular operations or actionsby virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect includes a processing unit; a memory element coupled tothe processing unit; a means for communicating over a network; encodedinstructions stored in the memory element, which when implemented by theprocessing unit, configure the computer automated system to: in realtime, aggregate data from a plurality of data sources connected to thenetwork; normalize the data aggregated from the plurality of datasources. The processing unit is configured to analyze the aggregated,normalized data based on a correlated event or events, a correlatedcondition or conditions, and a correlated trend or trends across theplurality of data sources. The processing unit also determines a userneed based on the analyzed aggregated, normalized data. The user needincludes a need for an item or items including at least one of aservice, a product, and an upgrade of hardware or software components.The processing unit also determines a provider from a plurality ofproviders based on the determined user need. The processing unit alsoincludes manually or automatically initiating a need fulfillmenttransaction between the user and the provider. Other embodiments of thisaspect include corresponding computer systems, apparatus, and computerprograms recorded on one or more computer storage devices, eachconfigured to perform the actions of the methods.

One general aspect includes in a computer automated system including aprocessing unit, a memory element coupled to the processing unit,communicating facility over a network, and encoded instructions, amethod including: in real time, aggregating data from a plurality ofdata sources connected to the network; normalizing the data aggregatedfrom the plurality of data sources. The computer automated method alsoincludes analyzing the aggregated, normalized data based on a correlatedevent or events, a correlated condition or conditions, and a correlatedtrend or trends across the plurality of data sources. The computerautomated method includes determining a user need based on the analyzedaggregated, normalized data. The user need includes a need for an itemor items including at least one of a service, a product, and an upgradeof hardware or software components. The computer automated system alsoincludes determining a provider from a plurality of providers based onthe determined user need. The computer automated system also includesmanually or automatically initiating a need fulfillment transactionbetween the user and the provider. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One general aspect includes a mobile wireless communication deviceincluding: a processing unit; a memory element coupled to the processingunit; encoded instructions that configure the mobile device to, inreal-time: aggregate data from a plurality of data sources, where thesaid plurality of data sources include a single or plurality of proxydevices, legacy protocols, devices, applications, machines, sensors,things across locations and user types, or device clouds among devicesand applications; normalize data from the said plurality of datasources; analyze the aggregated, normalized data based on a correlatedevent or events, a correlated condition or conditions, and a correlatedtrend or trends across the plurality of data sources; and based on theanalyzed data, combine the aggregated and normalized data, and displaythe combined data in a display compatible format. The mobile wirelesscommunication device also includes determining a user need based on theanalyzed aggregated, normalized data. The user need includes a need foran item or items including at least one of a service, a product, and anupgrade of hardware or software components. The mobile wirelesscommunication device also includes determining a provider from aplurality of providers based on the determined user need. The mobilewireless communication device also includes initiating a needfulfillment transaction between the user and the provider. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the computer system according to an embodiment.

FIG. 2 illustrates data normalization work flow according to anembodiment.

FIG. 3 illustrates the work flow according to an embodiment.

FIG. 4 is a simplified illustration of an embodiment.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. In other instances, well-knownfeatures have not been described in detail to avoid obscuring theinvention.

Embodiments disclosed include a platform for collecting, normalizing,aggregating, and presenting/processing data over a wide range of devicesand applications.

An embodiment includes a computer automated system comprising aprocessing unit, a memory element coupled to the processing unit, and ameans or capability for communicating over a network. FIG. 1 illustratesthe computer system according to an embodiment. The computer automatedsystem includes a data aggregation server 102, a data normalizationengine 104, and a presentation platform comprising a graphical userinterface 106. In the computer system the data aggregation server isconfigured to aggregate data from a plurality of devices 110 andapplications, such as Medical CT scanners 122, Medical MRI machines,124, Printers 126, Universal Power Supply (UPS) devices, 128, etc. Thedata normalization engine104 is configured to normalize data from thesaid plurality of devices and applications. And the presentationplatform is configured to combine the aggregated and normalized data,and display the combined data in a display compatible format.

According to an embodiment the computer system is further configured toabstract a plurality of classes of devices. This abstracting is done viaa data modeling language comprised in the configuration of the computersystem. The plurality of classes of devices can include medical CTscanners, medical MRI systems, printers, and UPS stations. Thedata-modeling language is used to create an abstract data-model for adevice-class (e.g. Medical CT scanners, Medical MRI systems, Printers,UPS stations, etc.). The data-model contains a list of device-classparameters, their types, whether (say) read-only or read/write, andsynchronous or asynchronous access and propagation, among other things.According to an embodiment, a template (or templates) is/are created toaccess data-model parameters for a particular device model. The templatecontains a mapping from the data-model parameters to one or moreconnectors, specifying the connector name, address/index within theconnector space, and processing instructions to read/write eachparameter in the data-model for the particular device model. Connectorsfor each data-access method such as a network protocol (e.g. SNMPconnector 134, HTTP connector 136), or/and Log File connector 132,whichcan read/write device parameters based on information in the templatefor a particular device model can be used. Parameters for each deviceunder management are either polled periodically or on-demand. Inaddition to periodic or on-demand polling, data can be collected fromdata sources, asynchronously.

Device data is collected using several methods. According to anembodiment, an embedded data collection stack resides on the device. Thedata collections stack interfaces directly with the device using thedevice application programming interface. In an alternate embodiment,the embedded data collection stack resides in a network gateway device.Here the data collections stack interfaces with the concerned deviceover the local area network using a network protocol supported by theconcerned device. In some embodiments, the gateway device may bepre-loaded with the stack by the manufacturer, and proxy software thatresides on and configures a computer in the IP network of the device. Inyet another alternate embodiment, the data collection stack can beinstalled on a computer in the network by the network owner.Alternatively and additionally, it can be installed on an appliance thatis inserted into the network. In the network is also a device cloud thatcan aggregate data for a single or plurality of types of devices, anddevices. Data is collected from the device cloud database using theTCP/IP protocol or any other compatible equivalent protocol. Anembodiment comprises a mobile application, or part of a mobileapplication that resides on and configures a mobile device, such thatthe data collection stack is activated when the mobile applicationexecutes. In some instances, the application can be a background processon the mobile device that runs continuously.

In the computer system, the data normalization engine further comprisesa plurality of extensible connectors (such as Log File Connector 132,SNMP Connector 134, HTTP connector 136), which are used to extract atleast one of log file data, SNMP data and HTTP data from each of aplurality of connected devices. Shown in FIG. 1 are devices 110, whichinclude CT1 111, CT2 112, CT1 113, MRI1 114, MRI2 115, MRI1 116,Printer1 117, and UPS1 118. Further the system is accordingly configuredto map data-model parameters to variables in device models via apreviously created plurality of templates. The said plurality ofextensible connectors is further configured to extract the data modelparameters mapped to the said variables in the said device models.Additionally, each device parameter is stored as a name value pair in ano-schema database.

According to an embodiment the computer automated system can beconfigured to canonical-ize all normalized and aggregated data. Thiscanonicalization includes simplifying the data by adding Meta data andderived data to device data via a set of workflows. In some embodiments,for example when aggregating data from sources not routed through theConnectors and Normalization path, the workflows also normalize thedata, if needed, before canonicalization. Further, via a plurality ofre-configurable widgets 140, which can include Graph Widget 142, ChartWidget 144, Table Widget 146, Form Widget 148, etc. and via a datamodeling language, the system can display device data in a plurality ofdifferent forms wherein the said plurality of different forms comprisesat least one of a graph, a chart, and a table. Preferably, the saidgraph, chart and table are configured to show data relevant to eachDevice Class.

Presentation across platforms/devices—once the data is aggregated andnormalized, it is stored in a schema-less data store. This allows anytype of data as ‘name-value’ pairs, indexed by a unique identifier foreach device. Access to this data is provided by web services, which areplatform independent and can be consumed by any client application onany platform or device.

Abstraction of a plurality of classes of devices using a data modellinglanguage—A data modelling language is used to capture all relevantinformation about a device class. This abstract data model represents adevice class in general. All concrete manifestations of the device class(different models from different manufacturers) can be mapped onto thedata model of the device class.

The data model representation itself is not specific to a device class.It is a general representation that can be used for any device class. Itcontains the names of the parameters of a device class, their valuetypes, the unique device identifier, variables that can be used toidentify the devices of that class, the protocols it responds to for thedifferent variables both synchronous and asynchronous, and the frequencyof polling for different variables.

FIG. 2 illustrates data normalization according to an embodiment. Datamodel 201 comprises data-modeling language instrumental in creating anabstract data-model for a device-class (e.g. Medical CT, Medical MRI,Printers, and UPS etc.). The data-model contains a list of device-classparameters, their types, whether read-only or read/write, andsynchronous or asynchronous access and propagation, among other things.

Template 202 is created to access data-model parameters for a particulardevice model. The template contains a mapping from the data-modelparameters to one or more connectors, specifying the connector name,address/index within the connector space, and processing instructions toread/write each parameter in the data-model for the particular devicemodel.

Connectors 203 are available for each data-access method such as anetwork protocol (e.g. SNMP, HTTP), which can read/write deviceparameters based on information in the template for a particular devicemodel.

Parameters for each device under management 204 are either polledperiodically or on-demand. In addition to periodic or on-demand polling,data can be collected from data sources, asynchronously.

The abstract data-model now contains <name, value> tuples for eachinstance of a device belonging to device-class. These tuples (names) arenecessarily normalized 205 for all devices belonging to a device-class,based on the definition of the data-model for the device-class.

The normalized data for all devices in a device-class, and acrossdevice-classes, is now aggregated into a schema-less data-base 206 to bestored as <name, value> tuples.

The normalized data stored in the data-base206 is processed by a set ofdevice-model independent, but device-class specific, workflows 207 toadd metadata and derived data to the extracted device parameter data.

A set of configurable widgets 208 to display device-data and associatedmeta-data/derived-data in different forms (graphs, charts, tables etc.)complete the canonical viewing of the data.

An embodiment includes a computer implemented method comprisingaggregating data from a plurality of devices and applications in a dataaggregation server capable of communicating and aggregating the saiddata over a wired or wireless network. The method further includes, in adata normalization engine, normalizing data from a plurality of devicesand applications. And additionally, combining the aggregated andnormalized data, and displaying the combined data in a displaycompatible format through a presentation platform comprising a graphicaluser interface.

The method further includes abstracting a plurality of classes ofdevices via a data modeling language. The said abstracting comprisesabstracting at least one of medical CT scanners, medical MRI machines,printers, and UPS stations.

FIG. 3 illustrates the work flow according to an embodiment. Step 301includes aggregating data from a plurality of devices and applications.Step 302 triggers normalization of data from the plurality of devicesand applications. Normalization comprises generating an abstractdata-model for a device-class (e.g. Medical CT, Medical MRI, Printers,UPS etc.), as shown in step 302 a. Step 302 b comprises generatingtemplates to access data-model parameters for a particular device model.Step 302 c comprises selection of an access method via a single orplurality of connectors. Step 302 d includes polling of parameters foreach device under management. Step 302 e includes aggregating thenormalized data for all devices in a device-class, and acrossdevice-classes into a schema-less data-base. Step 302 f includes addingmetadata and derived data to the extracted device parameter data. Step303 includes combining the aggregated and normalized data, anddisplaying the combined data in a display compatible format through apresentation platform comprising a graphical user interface. Step 304includes abstracting the plurality of classes of devices. Step 305includes a self-learning step. Based on collected normalized data fromdevices, applications and users, the computer system is caused toheuristically form associations of users to devices that they use, andthus create clusters or groups. With location data, geo locationcapability could map these groups to be homes, offices, schools,factories etc. Step 306 enhances the heuristic machine learningcapability with predictive analytics. Analytics of collected normalizeddata from devices, applications and users leads todetection/identification and prediction of needs of users and theirdevices. Examples of needs could be service, supplies replenishment,upgrade/diagnostics of software, etc. These predictions can be arrivedat either by prior thresholds being met or via machine learning.Embodiments disclosed include manual and automated computer systems.Manual systems include an extra layer, step 307 wherein designation ofadministrator(s) devices enables addressing needs identified in 306.This can also be automated via nomination or/and designation via anapplication user interface during registration process for larger numberof users within a group.

Step 308 a includes matching identified needs of users with providersover the network. Based on the needs determined in 306, the computersystem triggers a search over the network, and based on the returnedresults, a selection of matching of providers of the need(s) based oncategory of need/preference/geography/capability of provider, context,etc.

Step 308 b includes automating the transaction process and preferablywith contextual offers in a transaction. Based on the needs determinedin 306, contextual commercial offers from selected provider(s) areobtained and presented to administrators designated as per 307-308 b.Alternatively, a user preference is referenced to automate a contextualtransaction.

Step 308 c includes completing a contextual transaction. Based on theoffers presented in 308 b, the administrator accepts an offer throughassociated workflows of the provider and the contextual need is met.Alternatively the transaction is automated based on a pre-configureduser preference.

In an embodiment, the said normalizing of data includes extracting atleast one of a log file data, an SNMP data and HTTP data from each of aplurality of connected devices via a plurality of extensible connectors.The method further includes mapping data-model parameters to variablesin device models via a plurality of pre-created templates. Andextracting the data model parameters mapped to the said variables in thesaid device models via the said plurality of extensible connectors.

FIG. 4 is a simplified illustration of an embodiment. Preferably thesystem and method are configured to loop through a return path in threesteps or stages: Normalization of data→Analysis andinsights→Decision/Action in real time.

The normalization 401 entails data collection and normalization across avast and disparate amount of data sources. Normalization includesnormalization across proxy devices, legacy protocols, devices, machines,and sensors, things across locations and user types, and device cloudsamong others.

According to an embodiment, if the data is coming through an associatedor recognized proxy, the data is normalized at the collection point orat an edge. According to an alternate embodiment, if the data is comingthrough 3^(rd) party sources or channels then normalization isimplemented at an associated server. Additionally, there could beinstances of the data coming through different sources as well—e.g.Mobile, the associated proxy, through web services etc. In suchinstances, post processing and normalization is done at the associatedserver. Variations, modifications, permutations, and combinations arepossible, as would be apparent to a person having ordinary skill in theart.

The Analysis 402 is based on a correlated event or events, a correlatedcondition or conditions, and a correlated trend or trends across thesedisparate sources of data, analyzed via Device Internet of Things (JOT)stacks and gateways.

This is followed by Decisions/Actions 403 as a return path or closedloop back to the machines/sensors and or other devices/applications.Decisions are taken in real-time based on information obtained from datasources, like the web, social media, etc. and communication devices, inreal-time.

Prior art systems offer sequential steps and the time taken to gothrough these steps are very slow and by the time a decision is taken,the data/conditions may have changed leading to wrong decisions. Also,those that offer such solutions tend to be silos of a single context orvendor or type of device. The decision making is based on analysis ofconditions across multiple sources that need to be correlated and donein real-time. Embodiments disclosed enable this correlation and furtherenable automation of the disclosed systems and methods such thatpreferred embodiments are configured to react in real-time, dependingupon prevailing conditions.

Since various possible embodiments might be made of the above invention,and since various changes might be made in the embodiments above setforth, it is to be understood that all matter herein described or shownin the accompanying drawings is to be interpreted as illustrative andnot to be considered in a limiting sense. Thus it will be understood bythose skilled in the art that although the preferred and alternateembodiments have been shown and described in accordance with the PatentStatutes, the invention is not limited thereto or thereby.

Embodiments disclosed enable seamless communication across device andmachine models, applications, brands, operating systems, networks, andprotocols. Embodiments disclosed further enable real-time datacollection and normalization, real-time analysis based on events,conditions, and trends across disparate data sources, and real-timedecision making/action as a return path or closed loop back to themachines/sensors and or other devices/applications. Embodimentsdisclosed also enable the return path or the control path forconfiguration, control and diagnostics. Preferred embodiments include acorrelation capability across the multiple sources in real-time analysisand decision making. Embodiments disclosed also enable working acrossmultiple silos.

The figures illustrate the architecture, functionality, and operation ofpossible implementations of systems and methods according to variousembodiments of the present invention. It should also be noted that, insome alternative implementations, the functions noted/illustrated mayoccur out of the order noted in the figures. For example, two blocksshown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

In general, the steps executed to implement the embodiments of theinvention, may be part of an automated or manual embodiment, andprogrammable to follow a sequence of desirable instructions.

The present invention and some of its advantages have been described indetail for some embodiments. It should be understood that although someexample embodiments of the data aggregation, normalization andpresentation system and method are described with reference to specificdevice types and applications, the computer implemented system andmethod is highly reconfigurable, and embodiments include reconfigurablesystems that may be dynamically adapted to be used in other contexts aswell. It should also be understood that various changes, substitutionsand alterations can be made herein without departing from the spirit andscope of the invention as defined by the appended claims. An embodimentof the invention may achieve multiple objectives, but not everyembodiment falling within the scope of the attached claims will achieveevery objective. Moreover, the scope of the present application is notintended to be limited to the particular embodiments of the process,machine, manufacture, and composition of matter, means, methods andsteps described in the specification. A person having ordinary skill inthe art will readily appreciate from the disclosure of the presentinvention that processes, machines, manufacture, compositions of matter,means, methods, or steps, presently existing or later to be developedare equivalent to, and fall within the scope of, what is claimed.Accordingly, the appended claims are intended to include within theirscope such processes, machines, manufacture, compositions of matter,means, methods, or steps.

The invention claimed is:
 1. A computer automated system comprising: aprocessing unit; a memory element coupled to the processing unit; acommunications network; encoded instructions stored in the memoryelement, which when implemented by the processing unit, configure thecomputer automated system to: in real time, aggregate device data via an embedded data collection stack from a plurality of device classesconnected to the network; wherein the aggregation of device data furthercomprises aggregation of device behavior data, device usage data, anddevice location data; normalize the device data aggregated from theplurality of device classes; wherein the normalize the device datacomprises: generating an abstract device data model for the plurality ofdevice classes; extracting a device data model para meter via thegenerated abstract device data model; polling the extracted device datamodel parameter extracted via the generated a bstract device data model;adding metadata to the extracted device data model parameter; addmeta-data and derived data to aggregated device data from each of theplurality of device classes; analyze the aggregated, normalized devicedata based on a correlated event or events and a correlated condition orconditions across the plurality of device classes; and determine adevice behavior based on the analyzed aggregated, normalized data;wherein the determined device behavior comprises determining a need foran item or items comprising at least one of a service, a product, and anupgrade of hardware or software components; determine a provider from aplurality of providers based on the determined device behavior; andinitiate a need fulfillment transaction between the device and theprovider.
 2. The computer automated system of claim 1 wherein the systemis further configured to: heuristically form an association orassociations of devices based on at least one of their determined needs,their determined providers and their determined location.
 3. Thecomputer automated system of claim 1 wherein the system is furtherconfigured to: determine the device need based on a pre-definedthreshold being met or via heuristic machine learning.
 4. The computerautomated system of claim 1 wherein the system is further configured to:fulfill the need fulfillment transaction via a pre-designatedadministrator.
 5. The computer automated system of claim 1 wherein thesystem is further configured to: based on the determined device need,initiate via the network, a search for a single or plurality ofproviders connected to the network; select the single or plurality ofproviders based on at least one of a category, a preference, ageography, and a context; and initiate and complete the need fulfillmenttransaction between the device and the provider.
 6. The computerautomated system of claim 1 wherein: the said plurality of deviceclasses comprise a single or plurality of proxy devices, legacyprotocols, devices, applications, machines, and sensors, acrosslocations; and the device usage data comprises at least one of devicemanufacturer, model name, serial number, settings, status, consumablestatus, and user details.
 7. The computer automated system of claim 1wherein the system is further configured to: abstract the plurality ofdevice classes, wherein the device classes comprise a plurality of CTscanners, medical MRIs, printers, and UPS systems.
 8. The computerautomated system of claim 1 wherein normalizing the data aggregated fromthe plurality of device classes comprises, via a plurality of extensibleconnectors: extracting at least one of log file data, SNMP data and HTTPdata from each of the plurality of connected devices; and extractingdata model parameters mapped to variables in device models.
 9. Thecomputer automated system of claim 1 wherein the system is configuredto: display device data as at least one of a graph, a chart, and a tablewherein the said graph, chart and table are configured to show datarelevant to each data source class.
 10. The system of claim 1 whereincomputer system is further configured to: analyze the aggregated,normalized device data via a single or plurality of Device Internet ofThings (TOT) stacks and gateways; based on the analysis, implement asingle or plurality of decisions, in real-time, in a return path, on asingle or plurality of machines, sensors, devices or applications; andimplementing the single or plurality of decisions in the return path orclosed loop comprises contextual real-time matching between a deviceclass with a single or plurality of providers according to thedetermined device need.
 11. In a computer automated system comprising aprocessing unit, a memory element coupled to the processing unit,communicating facility over a network, and encoded instructions, amethod comprising: in real time, aggregating device data via an embeddeddata stack from a plurality of device classes connected to the network;wherein aggregating device data further comprises aggregating devicebehavior data, device usage data, and device location data; normalizingthe device data aggregated from the plurality of device classes; whereinnormalizing the device data comprises: generating an abstract devicedata model for the plurality of device classes; extracting a device datamodel parameter via the generated abstract device data model; pollingthe extracted device data model parameter extracted via the generatedabstract device data model; adding metadata to the extracted device datamodel parameter; adding meta-data and derived data to aggregated devicedata from each of the plurality of device classes; analyzing theaggregated, normalized device data based on a correlated event or eventsand a correlated condition or conditions across the plurality of deviceclasses; and determining a device behavior based on the analyzedaggregated, normalized device data; wherein the determined devicebehavior comprises determining a need for an item or items comprising atleast one of a service, a product, and an upgrade of hardware orsoftware components; determining a provider from a plurality ofproviders based on the determined device behavior; and initiating a needfulfillment transaction between the device and the provider.
 12. Themethod of claim 11 further comprising: heuristically forming anassociation or associations of devices and at least one of theirdetermined needs, their determined providers and their determinedlocation.
 13. The method of claim 11 further comprising: determining thedevice need based on a pre-defined threshold being met or via heuristicmachine learning.
 14. The method of claim 11 further comprising:fulfilling the need fulfillment transaction via a pre-designatedadministrator.
 15. The method of claim 11 further comprising: based onthe determined device need, initiating via the network, a search for asingle or plurality of providers connected to the network; selecting thesingle or plurality of providers based on at least one of a category, apreference, a geography, and a context; and initiating and completingthe need fulfillment transaction between the device and the provider.16. The method of claim 11 wherein: the said plurality of device classescomprise a single or plurality of proxy devices, legacy protocols,devices, applications, machines, and sensors, across locations; and thedevice usage data comprises at least one of device manufacturer, modelname, serial number, settings, status, consumable status, and userdetails.
 17. The method of claim 11 further comprising: abstracting theplurality of device classes, wherein the plurality of device classescomprise a plurality of CT scanners, medical MRIs, printers, and UPSsystems.
 18. The method of claim 11 wherein normalizing the dataaggregated from the plurality of device classes comprises, via aplurality of extensible connectors: extracting at least one of log filedata, SNMP data and HTTP data from each of the plurality of connecteddevices; and extracting data model parameters mapped to variables indevice models.
 19. The method of claim 11 further comprising: displayingdevice data as at least one of a graph, a chart, and a table wherein thesaid graph, chart and table are configured to display data relevant toeach data source class.
 20. The method of claim 11 further comprising:analyzing the aggregated, normalized device data via a single orplurality of Device Internet of Things (IOT) stacks and gateways; basedon the analysis, implementing a single or plurality of decisions, inreal-time, in a return path, on a single or plurality of machines,sensors, devices or applications; and wherein implementing the single orplurality of decisions in the return path comprises contextual real-timematching between a device class with a single or plurality of providersaccording to the determined device need.
 21. In a computer automatedsystem comprising a processing unit coupled to a memory element andhaving instructions encoded thereon, a method comprising, in real-time:aggregating device data via an embedded data collection stack from aplurality of device classes, wherein the said plurality of deviceclasses comprise a single or plurality of proxy devices, legacyprotocols, devices, applications, machines, and sensors, acrosslocations; normalizing said device data from the plurality of deviceclasses, wherein the normalizing comprises: generating an abstractdevice data model for the plurality of device classes; extracting devicedata model parameters via the generated abstract device data model;polling the extracted device data model parameters for each device classtype; adding metadata and derived data to the extracted device datamodel parameter; analyzing the aggregated and normalized device databased on a correlated event or events, and a correlated condition orconditions across the plurality of device classes; and based on the saidanalyzing: implementing a single or plurality of decisions, inreal-time, in a return path, on a single or plurality of machines,sensors, devices or applications; wherein the said implementingcomprises initiating a need fulfillment transaction between the deviceand a provider device; and combining the aggregated and normalizeddevice data, and displaying the combined device data in a displaycompatible format.
 22. The method of claim 21 wherein: the aggregatingthe device data further comprises aggregating device behavior data,device usage data, device location data; wherein the device usage datacomprises at least one of device manufacturer, model name, serialnumber, settings, status, consumable status, and user details; andwherein the said analyzing the aggregated, normalized device datafurther comprises determining a need for an item or items wherein thesaid item or items comprise at least one of a service, replacement, andan upgrade of hardware or software components.
 23. A mobile wirelesscommunication device comprising: a processing unit; a memory elementcoupled to the processing unit; encoded instructions that configure themobile device to, in real-time: aggregate device data via an embeddeddata collection stack from a plurality of device classes, wherein thesaid plurality of device classes comprise a single or plurality of proxydevices, legacy protocols, devices, applications, machines, and sensors,across locations; normalize device data from the said plurality ofdevice classes; wherein the normalize the device data comprises:generating an abstract device data model for the plurality of deviceclasses; extracting a device data model parameter via the generatedabstract device data model; polling the extracted device data modelparameter extracted via the generated abstract device data model; addingmetadata to the extracted device data model parameter; add meta-data andderived data to aggregated device data from each of the plurality ofdevice classes; analyze the aggregated, normalized device data based ona correlated event or events, and a correlated condition or conditions,across the plurality of device classes; and based on the analyzed devicedata, combine the aggregated and normalized device data, and display thecombined device data in a display compatible format; determine a devicebehavior based on the analyzed aggregated, normalized device data;wherein the determined device behavior comprises determining a need foran item or items comprising at least one of a service, a product, and anupgrade of hardware or software components; determine a provider from aplurality of providers based on the determined device need; and initiatea need fulfillment transaction between the device and the provider. 24.The mobile wireless communication device of claim 23 wherein: theaggregation of device data further comprises aggregation of devicebehavior data, device usage data, and device location data; and whereinthe device usage data comprises at least one of device manufacturer,model name, serial number, settings, status, consumable status, and userdetails.
 25. The mobile wireless communication device of claim 23wherein the device is further configured to: based on the analyzeddevice data, trigger a single or plurality of decisions or actions, inreal-time, in a return path, on a single or plurality of machines,sensors, devices or applications.